Department of Computing
Imperial College London
180 Queen's Gate, London, SW7 2AZ; UK
Phone: +44 20 759 42920 (Fax: 48932)
Email: g.casale (at) imperial.ac.uk
I am a Lecturer (equivalent to US Assistant Professor) in the Department of Computing at Imperial College London. My current research interests focus on cloud performance engineering, measurement, and modeling. My Curriculum Vitae includes a list of my research activities and my service record. A quite complete list of my publications is available on DBLP.
Prospective PhD students: If you are a student interested in big data, cloud performance engineering, modelling, or multi-tier applications feel free to contact me via email to enquiry about PhD openings. Please attach a CV. PhD start dates are normally in April and October. Check here for financial support information from the College.
I recently started investigating the performance and reliability of cloud applications designed with the model-driven engineering paradigm to facilitate multi-cloud portability. My ongoing research in this area is sponsored by the MODAClouds FP7 IP project, see the vision paper:
I am interested in sizing and resource management for multi-tier applications, such as web sites. My research has focused in particular on characterising the impact of burstiness on performance. A typical example of burstiness is a sudden increase in the number of users accessing a web site. I also worked on techniques to carry out tests that can validate the ability of a system to cope with burstiness.
My research in this area has focused on developing techniques to fit empirical datasets and algorithms for statistical inference of metrics that are difficult, or impossible, to measure directly. An example of the latter is the average resource consumption of requests, also called the service demand. In fitting, I focused on a class of hidden Markov models called Markovian Arrival Processes (MAPs). MAPs can fit time series and are easy to embed in a continuous-time Markov chain. This provides a way to descrive in Markov chains events that arrive according to processes that are not Poisson, thus no more restrictions to exponential distributions. A good introduction is Chapter 2 in Alma Riska's thesis.
I have published a number of works on methodologies to analyse stochastic models that are common in performance evaluation, mainly queueing network models. Behind the mathematical curtain, these models capture phenomena that we experience daily, for example the sharing of a CPU by multiple processes or the waiting time to download a file. Queueing models represent a sensible way to answer what-if questions about a system experiencing contention and they are a useful decision support tool in many applications of operations research. My research in this area is mainly theoretical and focused on improving the tractability and accuracy of model solution techniques.
KPC-Toolbox (BSD License). Automatic fitting of time series into Markovan Arrival Processes (MAPs)
FUN AND MISCELLANEA
If English is your first language, my name is probably difficult to pronounce and spell. Actually, also if you are born in my country (Italy) you may pronunce it wrongly, normally adding a spurious 'gl' in the middle. If you want to learn how to pronunce my name, this website has a reasonable audio recording, although the wide open pronunciation of the 'a' is slighty different from the way it should be. I also keep track of interesting mispellings and errors, you can read the current list here, hopefully you won't contribute to it in the future. :-)
I am born in the beautiful city of Pavia at the south-west of Lombardy in northern Italy. If you are traveling to Milan or to the lakes, why not making a stop to Pavia as well? It's a renowned rice and wine production area, with a lot of middle age history, a big castle, and the beautiful Ticino river park. Few know that Pavia was the capital of Italy under the Lombard Kingdom, for about 200 years. Since then, Milan has become prominent in the area, but fortunately the good food still belongs to us. :-)